An Introduction to the Principle of Cognitive Unity in the Structure of KOSs.

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Title: An Introduction to the Principle of Cognitive Unity in the Structure of KOSs.
Authors: Amirhosseini, Maziar1 mazi_lib@yahoo.com
Source: Knowledge Organization. Jun2026, Vol. 53 Issue 3, p1-31. 31p.
Subjects: Cognitive structures, Theory of knowledge, Peirce, Charles S. (Charles Sanders), 1839-1914, Cognition, Cognitive learning theory
Abstract: This study investigates the cognitive structure of Knowledge Organization Systems (KOSs) and proposes the principle of cognitive unity as a foundational criterion for ranking and evaluating KOSs. The research aims to identify cognitive roots, dynamic cognitive units (data, information, and knowledge), and the forms of unity—both internal and dynamic—that govern their interrelationships. Employing a documentary research methodology, the study synthesizes theoretical frameworks from cognitive science, including Tall's concept of cognitive structure, Skemp's varifocal theory, and the theory of conceptual compression. The analysis integrates Peirce's triadic model for data, Semantic Link Network Theory (SLNT) for information, and Quantum Theory of Knowledge (QTK) for knowledge to delineate the constituent elements of each cognitive unit. The theory of concepts is used to represent the building blocks of knowledge, clarifying how concepts function as foundational units within the cognitive architecture of KOSs. These concepts serve as anchors for semantic and pragmatic structuring, enabling the formation of coherent knowledge systems. The study further applies Mandelblit and Zachar's model of dynamic patterns of correlation among elements to explain how cognitive units interact across levels. This dynamic correlation is complemented by the principle of internal unity, which identifies the structural coherence among constituent elements within each cognitive unit and supports the emergence of dynamic unity across units. The results demonstrate that knowledge, as the highest state of cognition, functions as the cognitive root encompassing the dynamic units of data and information. Each unit exhibits internal unity (Type 2) among its elements and dynamic unity (Type 4) across units, based on patterns of correlation and dialectical synthesis. These findings highlight the layered and interconnected nature of KOSs and affirm the value of cognitive analysis in their classification and development. The study concludes by introducing the principle of cognitive unity as a theoretical basis for ranking KOSs, with future research directed toward formalizing this principle and exploring its operational implications across diverse knowledge domains. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge Organization is the property of IMR Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: An Introduction to the Principle of Cognitive Unity in the Structure of KOSs.
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  Data: This study investigates the cognitive structure of Knowledge Organization Systems (KOSs) and proposes the principle of cognitive unity as a foundational criterion for ranking and evaluating KOSs. The research aims to identify cognitive roots, dynamic cognitive units (data, information, and knowledge), and the forms of unity—both internal and dynamic—that govern their interrelationships. Employing a documentary research methodology, the study synthesizes theoretical frameworks from cognitive science, including Tall's concept of cognitive structure, Skemp's varifocal theory, and the theory of conceptual compression. The analysis integrates Peirce's triadic model for data, Semantic Link Network Theory (SLNT) for information, and Quantum Theory of Knowledge (QTK) for knowledge to delineate the constituent elements of each cognitive unit. The theory of concepts is used to represent the building blocks of knowledge, clarifying how concepts function as foundational units within the cognitive architecture of KOSs. These concepts serve as anchors for semantic and pragmatic structuring, enabling the formation of coherent knowledge systems. The study further applies Mandelblit and Zachar's model of dynamic patterns of correlation among elements to explain how cognitive units interact across levels. This dynamic correlation is complemented by the principle of internal unity, which identifies the structural coherence among constituent elements within each cognitive unit and supports the emergence of dynamic unity across units. The results demonstrate that knowledge, as the highest state of cognition, functions as the cognitive root encompassing the dynamic units of data and information. Each unit exhibits internal unity (Type 2) among its elements and dynamic unity (Type 4) across units, based on patterns of correlation and dialectical synthesis. These findings highlight the layered and interconnected nature of KOSs and affirm the value of cognitive analysis in their classification and development. The study concludes by introducing the principle of cognitive unity as a theoretical basis for ranking KOSs, with future research directed toward formalizing this principle and exploring its operational implications across diverse knowledge domains. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge Organization is the property of IMR Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.31083/KO47986
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        Text: English
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              Text: Jun2026
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